Open Access
ARTICLE
Survey of Knowledge Graph Approaches and Applications
Hangjun Zhou1, Tingting Shen1, *, Xinglian Liu1, Yurong Zhang1, Peng Guo1, 2, Jianjun Zhang3
1 Hunan University of Finance and Economics, Changsha, China.
2 University Malaysia Sabah, Kota Kinabalu, Malaysia.
3 Hunan Normal University, Changsha, China.
* Corresponding Author: Tingting Shen. Email: .
Journal on Artificial Intelligence 2020, 2(2), 89-101. https://doi.org/10.32604/jai.2020.09968
Received 01 February 2020; Accepted 13 March 2020; Issue published 15 July 2020
Abstract
With the advent of the era of big data, knowledge engineering has received
extensive attention. How to extract useful knowledge from massive data is the key to big
data analysis. Knowledge graph technology is an important part of artificial intelligence,
which provides a method to extract structured knowledge from massive texts and images,
and has broad application prospects. The knowledge base with semantic processing
capability and open interconnection ability can be used to generate application value in
intelligent information services such as intelligent search, intelligent question answering
and personalized recommendation. Although knowledge graph has been applied to various
systems, the basic theory and application technology still need further research. On the
basis of comprehensively expounding the definition and architecture of knowledge graph,
this paper reviews the key technologies of knowledge graph construction, including the
research progress of four core technologies such as knowledge extraction technology,
knowledge representation technology, knowledge fusion technology and knowledge
reasoning technology, as well as some typical applications. Finally, the future development
direction and challenges of the knowledge graph are prospected.
Keywords
Cite This Article
H. Zhou, T. Shen, X. Liu, Y. Zhang, P. Guo
et al., "Survey of knowledge graph approaches and applications,"
Journal on Artificial Intelligence, vol. 2, no.2, pp. 89–101, 2020.